store = {}
store['args']={'name': 'bald_mnist_804264', 'type': 'AcquisitionFunction.bald', 'seed': 804264, 'experiment_description': 'Coreset BALD vs BALD', 'acquisition_method': 'AcquisitionMethod.independent', 'available_sample_k': 1, 'num_inference_samples': 20, 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'target_accuracy': 0.96, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.mnist', 'initial_samples': [38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329], 'experiment_task_id': 11, 'experiments_laaos': './experiment_configs/coreset_bald_vs_bald/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=11', '--experiments_laaos=./experiment_configs/coreset_bald_vs_bald/configs.py']
store['iterations']=[]
store['initial_samples']=[38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6172, 'nll': 2.5905902904510496}, 'chosen_samples': ['59355'], 'chosen_samples_score': [1.2267580061002783], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6375, 'nll': 2.6809482082366944}, 'chosen_samples': ['16669'], 'chosen_samples_score': [1.2542093671951824], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7145, 'nll': 1.934323826789856}, 'chosen_samples': ['19820'], 'chosen_samples_score': [1.2956714172160586], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6847, 'nll': 1.9765396076202393}, 'chosen_samples': ['21805'], 'chosen_samples_score': [1.1530900658626941], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7126, 'nll': 1.8031031387329102}, 'chosen_samples': ['43565'], 'chosen_samples_score': [1.204083574683262], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6638, 'nll': 2.036704122924805}, 'chosen_samples': ['49157'], 'chosen_samples_score': [1.162355974450044], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7359, 'nll': 1.529014629173279}, 'chosen_samples': ['27177'], 'chosen_samples_score': [1.1191058820959419], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7225, 'nll': 1.5614769519805909}, 'chosen_samples': ['55496'], 'chosen_samples_score': [1.2447560475549588], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7495, 'nll': 1.4504256797790527}, 'chosen_samples': ['49784'], 'chosen_samples_score': [1.2978249890466973], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7379, 'nll': 1.5766815673828125}, 'chosen_samples': ['39518'], 'chosen_samples_score': [1.21060417326022], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.764, 'nll': 1.3917169845581054}, 'chosen_samples': ['39597'], 'chosen_samples_score': [1.137311835941559], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7461, 'nll': 1.50063796043396}, 'chosen_samples': ['3492'], 'chosen_samples_score': [1.2402506568037246], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7606, 'nll': 1.4628647735595703}, 'chosen_samples': ['22715'], 'chosen_samples_score': [1.19166268332883], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7696, 'nll': 1.3694882873535157}, 'chosen_samples': ['36531'], 'chosen_samples_score': [1.127029393349075], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.788, 'nll': 1.2065212781906127}, 'chosen_samples': ['32022'], 'chosen_samples_score': [1.1229316805648133], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8133, 'nll': 1.0655711147308349}, 'chosen_samples': ['30487'], 'chosen_samples_score': [1.0640949359494383], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.797, 'nll': 1.1277079975128175}, 'chosen_samples': ['17336'], 'chosen_samples_score': [1.0499477448271868], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8228, 'nll': 1.0370067895889281}, 'chosen_samples': ['41140'], 'chosen_samples_score': [1.1856188682699358], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8276, 'nll': 1.0194157329559326}, 'chosen_samples': ['33013'], 'chosen_samples_score': [1.024195195240508], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7958, 'nll': 1.1743568775177002}, 'chosen_samples': ['4263'], 'chosen_samples_score': [1.0397509284967636], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8123, 'nll': 1.0421151823043824}, 'chosen_samples': ['4935'], 'chosen_samples_score': [1.0875906063167573], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8139, 'nll': 1.0876961681365966}, 'chosen_samples': ['13167'], 'chosen_samples_score': [1.0605805907147952], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.812, 'nll': 1.0889022005081177}, 'chosen_samples': ['5728'], 'chosen_samples_score': [1.1301020833071438], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.829, 'nll': 0.9609217298507691}, 'chosen_samples': ['33196'], 'chosen_samples_score': [0.9820446273826828], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8116, 'nll': 1.0312596870422364}, 'chosen_samples': ['13016'], 'chosen_samples_score': [1.0479787022631406], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8256, 'nll': 0.9861045732498169}, 'chosen_samples': ['50084'], 'chosen_samples_score': [0.9522767342017544], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8332, 'nll': 0.9678355466842652}, 'chosen_samples': ['38817'], 'chosen_samples_score': [1.0062118415701211], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8468, 'nll': 0.961870976638794}, 'chosen_samples': ['27328'], 'chosen_samples_score': [1.201159668673264], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8337, 'nll': 0.9523789442062378}, 'chosen_samples': ['54996'], 'chosen_samples_score': [1.056903282136726], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8349, 'nll': 0.9041673961639405}, 'chosen_samples': ['13030'], 'chosen_samples_score': [1.1792562475488144], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8164, 'nll': 0.9668847682952881}, 'chosen_samples': ['38269'], 'chosen_samples_score': [1.091695292645785], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8327, 'nll': 0.9472997388839721}, 'chosen_samples': ['52140'], 'chosen_samples_score': [0.9851758028551614], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8771, 'nll': 0.883323525428772}, 'chosen_samples': ['52012'], 'chosen_samples_score': [1.1998594399211098], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8602, 'nll': 0.900063002204895}, 'chosen_samples': ['17079'], 'chosen_samples_score': [1.2574910335411724], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8239, 'nll': 0.8991733741760254}, 'chosen_samples': ['20959'], 'chosen_samples_score': [0.984326434735729], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8412, 'nll': 0.8839073348999024}, 'chosen_samples': ['52830'], 'chosen_samples_score': [0.9773489464617071], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8324, 'nll': 0.9261298574447632}, 'chosen_samples': ['28368'], 'chosen_samples_score': [0.9641178272456443], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8422, 'nll': 0.8936245738983154}, 'chosen_samples': ['56742'], 'chosen_samples_score': [1.027491693622397], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8661, 'nll': 0.8451415802001954}, 'chosen_samples': ['26733'], 'chosen_samples_score': [1.162375629978836], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8467, 'nll': 0.9433256065368653}, 'chosen_samples': ['29132'], 'chosen_samples_score': [1.1102637801327733], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8537, 'nll': 0.941502319908142}, 'chosen_samples': ['12304'], 'chosen_samples_score': [1.1343213610820444], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.838, 'nll': 0.8470028232574462}, 'chosen_samples': ['5106'], 'chosen_samples_score': [0.9965163977729219], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8584, 'nll': 0.9007690591812134}, 'chosen_samples': ['5086'], 'chosen_samples_score': [1.1333397810405725], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8484, 'nll': 0.7906392225265503}, 'chosen_samples': ['2845'], 'chosen_samples_score': [1.0043061663933979], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8395, 'nll': 0.8424443353652954}, 'chosen_samples': ['59446'], 'chosen_samples_score': [0.9413500315546965], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8788, 'nll': 0.8496386772155762}, 'chosen_samples': ['49567'], 'chosen_samples_score': [1.1694784810187018], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8789, 'nll': 0.8625926870346069}, 'chosen_samples': ['26372'], 'chosen_samples_score': [1.1341953933638944], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8732, 'nll': 0.815839433670044}, 'chosen_samples': ['49515'], 'chosen_samples_score': [1.1125330964346536], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8539, 'nll': 0.9466866144180298}, 'chosen_samples': ['30588'], 'chosen_samples_score': [1.1331979816723878], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8744, 'nll': 0.7680645011901855}, 'chosen_samples': ['55353'], 'chosen_samples_score': [1.0599453634738292], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8691, 'nll': 0.7937972936630249}, 'chosen_samples': ['6418'], 'chosen_samples_score': [1.058175197388549], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8433, 'nll': 0.8640504096984863}, 'chosen_samples': ['19942'], 'chosen_samples_score': [0.9562781402169648], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8662, 'nll': 0.7941953536987305}, 'chosen_samples': ['47506'], 'chosen_samples_score': [1.119744472730084], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8481, 'nll': 0.8985341375350953}, 'chosen_samples': ['39567'], 'chosen_samples_score': [1.1079294196970377], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.863, 'nll': 0.7915725891113281}, 'chosen_samples': ['54106'], 'chosen_samples_score': [1.09866017414163], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8757, 'nll': 0.752396992111206}, 'chosen_samples': ['47646'], 'chosen_samples_score': [1.186354086706336], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8643, 'nll': 0.7676728973388672}, 'chosen_samples': ['47132'], 'chosen_samples_score': [1.0031254008752168], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8835, 'nll': 0.7600189842224121}, 'chosen_samples': ['26050'], 'chosen_samples_score': [1.111596586921722], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8556, 'nll': 0.8128727415084839}, 'chosen_samples': ['47651'], 'chosen_samples_score': [0.8289056837306474], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.892, 'nll': 0.7044373046875}, 'chosen_samples': ['5315'], 'chosen_samples_score': [1.1979888889769712], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8821, 'nll': 0.7137852451324463}, 'chosen_samples': ['12497'], 'chosen_samples_score': [1.0577428552972639], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8674, 'nll': 0.7539944215774537}, 'chosen_samples': ['8771'], 'chosen_samples_score': [1.020780191542713], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8844, 'nll': 0.7124657060623169}, 'chosen_samples': ['25960'], 'chosen_samples_score': [1.09452533956071], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8684, 'nll': 0.7966329504013061}, 'chosen_samples': ['14896'], 'chosen_samples_score': [1.1530255990657972], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8885, 'nll': 0.7546550145149231}, 'chosen_samples': ['10114'], 'chosen_samples_score': [1.2458914352565011], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8805, 'nll': 0.750787633895874}, 'chosen_samples': ['16379'], 'chosen_samples_score': [1.1390633520907643], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8691, 'nll': 0.7668749021530151}, 'chosen_samples': ['3719'], 'chosen_samples_score': [1.0429428524757514], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9006, 'nll': 0.6898654603004456}, 'chosen_samples': ['47260'], 'chosen_samples_score': [1.215382249128367], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8907, 'nll': 0.7004897319793701}, 'chosen_samples': ['49002'], 'chosen_samples_score': [1.0062222702553922], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8738, 'nll': 0.7659748466491699}, 'chosen_samples': ['38298'], 'chosen_samples_score': [1.1293366468858952], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8825, 'nll': 0.6965776745796204}, 'chosen_samples': ['33650'], 'chosen_samples_score': [0.9779943528887678], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8882, 'nll': 0.7181331398010254}, 'chosen_samples': ['47741'], 'chosen_samples_score': [1.085221772165355], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.885, 'nll': 0.6900449033737183}, 'chosen_samples': ['50091'], 'chosen_samples_score': [0.994155208728073], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8741, 'nll': 0.7460229539871216}, 'chosen_samples': ['35018'], 'chosen_samples_score': [0.9825419853967912], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8681, 'nll': 0.7416412974357605}, 'chosen_samples': ['29320'], 'chosen_samples_score': [1.0522394700357023], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.886, 'nll': 0.706356876373291}, 'chosen_samples': ['29609'], 'chosen_samples_score': [1.0346813093947527], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8967, 'nll': 0.6427200396537781}, 'chosen_samples': ['5175'], 'chosen_samples_score': [0.9804204536081755], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8969, 'nll': 0.6475345113754273}, 'chosen_samples': ['53170'], 'chosen_samples_score': [0.9620945336334081], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8968, 'nll': 0.6629161032676697}, 'chosen_samples': ['44332'], 'chosen_samples_score': [1.0431227584306408], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8924, 'nll': 0.6458104001045227}, 'chosen_samples': ['6474'], 'chosen_samples_score': [1.011031367460908], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.895, 'nll': 0.6378995730400085}, 'chosen_samples': ['27323'], 'chosen_samples_score': [1.0158476494611461], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8735, 'nll': 0.7516485682487488}, 'chosen_samples': ['42334'], 'chosen_samples_score': [1.020042353841034], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8944, 'nll': 0.6503151849746704}, 'chosen_samples': ['18598'], 'chosen_samples_score': [1.0496961334100612], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9037, 'nll': 0.6796133779525757}, 'chosen_samples': ['52462'], 'chosen_samples_score': [1.1316248256691004], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8876, 'nll': 0.6592449362754822}, 'chosen_samples': ['9248'], 'chosen_samples_score': [1.0040373123298019], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8912, 'nll': 0.6561107175827027}, 'chosen_samples': ['24479'], 'chosen_samples_score': [1.0686971203620685], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9504, 'nll': 0.4279868507385254}, 'chosen_samples': ['55881'], 'chosen_samples_score': [1.1017552330534155], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9582, 'nll': 0.3834940119743347}, 'chosen_samples': ['12650'], 'chosen_samples_score': [1.0736326333846924], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9564, 'nll': 0.38479838461875915}, 'chosen_samples': ['50514'], 'chosen_samples_score': [1.0411757329596272], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9534, 'nll': 0.3705862518310547}, 'chosen_samples': ['1075'], 'chosen_samples_score': [1.1316256558094402], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9531, 'nll': 0.3763926417350769}, 'chosen_samples': ['2856'], 'chosen_samples_score': [1.0664422044811936], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9577, 'nll': 0.3686101565361023}, 'chosen_samples': ['5170'], 'chosen_samples_score': [1.0633291667633422], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9529, 'nll': 0.4163372231483459}, 'chosen_samples': ['48681'], 'chosen_samples_score': [1.2388153948983898], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9495, 'nll': 0.3924013235092163}, 'chosen_samples': ['28844'], 'chosen_samples_score': [0.9202838532626809], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9551, 'nll': 0.4072254159927368}, 'chosen_samples': ['46144'], 'chosen_samples_score': [1.0974549729910894], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9553, 'nll': 0.38959971780776975}, 'chosen_samples': ['48102'], 'chosen_samples_score': [1.079434653348481], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9535, 'nll': 0.38554893913269045}, 'chosen_samples': ['20110'], 'chosen_samples_score': [1.1035146082979082], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9634, 'nll': 0.35863740253448484}, 'chosen_samples': ['32784'], 'chosen_samples_score': [1.1174404846006447], 'chosen_samples_orignal_score': None})
